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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 示例：使用k近邻算法进行分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "\n",
    "# 生成数据\n",
    "# 生成60个训练样本，这60个样本在以 centers 参数指定中心点周围\n",
    "# cluster_std 是标准差，用来指明生成的点分布的松散程度\n",
    "centers = [[-2, 2], [2, 2], [0, 4]]\n",
    "X, y = make_blobs(n_samples=60, centers=centers, random_state=0, cluster_std=0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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     "output_type": "execute_result",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "import numpy as np \n",
    "\n",
    "# 画出数据\n",
    "plt.figure()\n",
    "c = np.array(centers)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=100, cmap='cool')  # 画出样本\n",
    "plt.scatter(c[:, 0], c[:, 1], s=100, marker='^', c='orange')  # 画出中心点\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用`KNeighborsClassifier`来训练模型，选择的参数k=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# 模型训练\n",
    "k = 5\n",
    "clf = KNeighborsClassifier(n_neighbors=k)\n",
    "clf.fit(X, y)\n",
    "\n",
    "# 进行预测\n",
    "X_sample = [[0, 2]]\n",
    "y_sample = clf.predict(x_sample)\n",
    "neighbors = clf.kneighbors(x_sample, return_distance=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要预测的样本是\\[0,2\\]，使用`kneighbors()`方法，把这个样本周围距离最近的5个点取出来，取出来的点是训练样本X里的索引。  \n",
    "通过绘图，把待预测的样本以及其最近的5个点标记出来："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(X[:, 0], X[:, 1], s=100, c=y, cmap='cool')  # 样本\n",
    "plt.scatter(c[:, 0], c[:, 1], s=100, marker='^', c='k') # 中心点\n",
    "plt.scatter(X_sample[0][0], X_sample[0][1], marker='x', c=y_sample, s=100, cmap='cool')  # 待预测的点\n",
    "\n",
    "for i in neighbors[0]:\n",
    "    # 预测点与距离最近的5个样本的连线\n",
    "    plt.plot([X[i][0], X_sample[0][0]], [X[i][1], X_sample[0][1]], 'k--', linewidth=0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 示例：使用k近邻算法进行回归拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成数据集，它在余弦曲线的基础上加入了噪声\n",
    "import numpy as np \n",
    "\n",
    "n_dots = 40\n",
    "X = 5 * np.random.rand(n_dots, 1)\n",
    "y = np.cos(X).ravel()\n",
    "\n",
    "# 添加一些噪声\n",
    "y += 0.2 * np.random.rand(n_dots) - 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练模型\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "k = 5\n",
    "knn = KNeighborsRegressor(k)\n",
    "knn.fit(X, y);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要怎么样进行回归拟合呢？  \n",
    "一个方法是，在X轴上的指定区间内生成足够多的点，针对这些足够密集的点，使用训练出来的模型进行预测，得到预测值`y_pred`，然后在坐标轴上，把所有的预测点连接起来，这样就画出了拟合曲线。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.9759725090987719"
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "# 生成足够密集的点进行预测\n",
    "T = np.linspace(0, 5, 500)[:, np.newaxis]\n",
    "y_pred = knn.predict(T)\n",
    "knn.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Text(0.5, 1.0, 'KNeighborsRegressor k = 5')"
     },
     "metadata": {},
     "execution_count": 21
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 把这些预测点连接起来，构成拟合曲线\n",
    "plt.figure()\n",
    "plt.scatter(X, y, c='g', label='data', s=100)  # 画出训练样本\n",
    "plt.plot(T, y_pred, c='k', label='prediction', lw=4)  # 画出拟合曲线\n",
    "plt.axis('tight')\n",
    "plt.title(f'KNeighborsRegressor k = {k}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 实例：糖尿病预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "dataset shape (768, 9)\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n0            6      148             72             35        0  33.6   \n1            1       85             66             29        0  26.6   \n2            8      183             64              0        0  23.3   \n3            1       89             66             23       94  28.1   \n4            0      137             40             35      168  43.1   \n\n   DiabetesPedigreeFunction  Age  Outcome  \n0                     0.627   50        1  \n1                     0.351   31        0  \n2                     0.672   32        1  \n3                     0.167   21        0  \n4                     2.288   33        1  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Pregnancies</th>\n      <th>Glucose</th>\n      <th>BloodPressure</th>\n      <th>SkinThickness</th>\n      <th>Insulin</th>\n      <th>BMI</th>\n      <th>DiabetesPedigreeFunction</th>\n      <th>Age</th>\n      <th>Outcome</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>6</td>\n      <td>148</td>\n      <td>72</td>\n      <td>35</td>\n      <td>0</td>\n      <td>33.6</td>\n      <td>0.627</td>\n      <td>50</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>85</td>\n      <td>66</td>\n      <td>29</td>\n      <td>0</td>\n      <td>26.6</td>\n      <td>0.351</td>\n      <td>31</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>183</td>\n      <td>64</td>\n      <td>0</td>\n      <td>0</td>\n      <td>23.3</td>\n      <td>0.672</td>\n      <td>32</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>89</td>\n      <td>66</td>\n      <td>23</td>\n      <td>94</td>\n      <td>28.1</td>\n      <td>0.167</td>\n      <td>21</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>137</td>\n      <td>40</td>\n      <td>35</td>\n      <td>168</td>\n      <td>43.1</td>\n      <td>2.288</td>\n      <td>33</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "data = pd.read_csv('../datasets/diabetes.csv')\n",
    "print(f'dataset shape {data.shape}')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从输出可以看到，总共有768个样本，8个特征，其中Outcome为标记值，0表示没有糖尿病，1表示有糖尿病。这8个特征分别如下：  \n",
    "* Pregnanacies: 怀孕的次数；\n",
    "* Gluecose: 血浆葡萄糖浓度，采用2小时口服葡萄糖耐量试验测得；\n",
    "* BloodPressure: 舒张压（毫米汞柱）\n",
    "* SkinThickness: 肱三头肌皮肤褶皱厚度（毫米）；\n",
    "* Insulin: 两个小时血清胰岛素（μU/毫升）；\n",
    "* BMI: 身体质量指数，体重除以身高的平方；\n",
    "* Diabetes Pedigree Function: 糖尿病血统指数，糖尿病和家庭遗传相关；\n",
    "* Age: 年龄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Outcome\n0    500\n1    268\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "# 进一步观察数据集里阳性和阴性样本的个数\n",
    "data.groupby('Outcome').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其中阴性样本为500例，阳性样本为268例。接着，需要对数据集进行简单处理，把8个特征值分离出来，作为训练集，把Outcome列分离出来作为目标值。然后把数据集划分为训练集和测试集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "shape of X (768, 8); shape of y (768,)\n"
    }
   ],
   "source": [
    "X = data.iloc[:, 0:8]\n",
    "y = data.iloc[:, 8]\n",
    "print(f'shape of X {X.shape}; shape of y {y.shape}')\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 模型比较 \n",
    "使用普通的k-均值算法，带权重的k-均值算法以及指定半径的k-均值算法分别对数据集进行拟合并计算评分："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "name: 0; socre: ('KNN', 0.7402597402597403)\nname: 1; socre: ('KNN with weights', 0.6948051948051948)\nname: 2; socre: ('Radius Neignbors', 0.6558441558441559)\n"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier, RadiusNeighborsClassifier\n",
    "\n",
    "# 构造3个模型\n",
    "models = []\n",
    "models.append((\"KNN\", KNeighborsClassifier(n_neighbors=2)))\n",
    "models.append((\"KNN with weights\", KNeighborsClassifier(n_neighbors=2, weights=\"distance\")))\n",
    "models.append((\"Radius Neignbors\", RadiusNeighborsClassifier(n_neighbors=2, radius=500.0)))\n",
    "\n",
    "# 分别训练3个模型，并计算评分\n",
    "results = []\n",
    "for name, model in models:\n",
    "    model.fit(X_train, y_train)\n",
    "    results.append((name, model.score(X_test, y_test)))\n",
    "\n",
    "for result in enumerate(results):\n",
    "    print(f\"name: {result[0]}; socre: {result[1]}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "权重算法，我们选择了距离越近，权重越高。RadiusNeighborsClassifier模型的半径，选择了500。从输出可以看出，普通的k-均值算法性能还是最好的。问题来了，这个判断准确么？答案是不准确的。因为我们的训练样本是随机分配的，不同的训练样本和测试样本组合可能导致计算出来的算法准确性是有差异的。  \n",
    "那怎么样才能更准确地对比算法的准确性呢？一个方法是，多次随机分配训练样本和交叉验证数据集，然后求模型准确性评分的平均值。通过scikit-learn提供的`KFold`和`cross_val_score`函数来处理这个问题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "name: KNN; socre: 0.7147641831852358\nname: KNN with weights; socre: 0.6770505809979495\nname: Radius Neignbors; socre: 0.6497265892002735\n"
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "\n",
    "results = []\n",
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=10)\n",
    "    cv_result = cross_val_score(model, X, y, cv=kfold)\n",
    "    results.append((name, cv_result))\n",
    "\n",
    "for result in results:\n",
    "    print(f\"name: {result[0]}; socre: {result[1].mean()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上述代码中，通过KFold把数据集分成10份，其中1份作为交叉验证数据集来计算模型准确性，剩余9份作为训练数据集，`cross_val_score()`函数总共计算出10次不同训练数据集和交叉验证数据集组合得到的模型准确性评分，最后求平均值。这样评价结果相对更加准确一些。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 模型训练及分析\n",
    "看起来，还是普通的k-均值算法性能更优一些。接下来，就是使用普通的k-均值算法对数据集进行训练，并查看对训练样本的拟合情况以及对测试样本的预测准确性情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "train score: 0.8338762214983714; test score: 0.7402597402597403\n"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=2)\n",
    "knn.fit(X_train, y_train)\n",
    "train_score = knn.score(X_train, y_train)\n",
    "test_score = knn.score(X_test, y_test)\n",
    "print(f\"train score: {train_score}; test score: {test_score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从输出结果可以看到两个问题，一是对训练样本的拟合情况不佳，评分才0.83多一些，这说明算法模型太简单了，无法很好拟合训练样本。二是模型的准确性欠佳，74%的预测准确性。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 特征选择及数据可视化\n",
    "使用scikit-learn在`sklearn.feature_selection`包里提供的`SelectKBest`来选择相关性最大的两个特征："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[148. ,  33.6],\n       [ 85. ,  26.6],\n       [183. ,  23.3],\n       [ 89. ,  28.1],\n       [137. ,  43.1]])"
     },
     "metadata": {},
     "execution_count": 35
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "\n",
    "selector = SelectKBest(k=2)\n",
    "X_new = selector.fit_transform(X, y)\n",
    "X_new[0:5] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对比前面开头的数据可以知道，相关性最大的两个特征分别是Glucose（血糖浓度）和BMI（身体质量指数）。血糖浓度跟糖尿病的有直接的相关性，而身体质量指数是反映肥胖程度的指标。  \n",
    "如果只使用这2个相关性最高的特征，3种不同的算法哪个准确性更高："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "name: KNN; socre: 0.725205058099795\nname: KNN with weights; socre: 0.6900375939849623\nname: Radius Neignbors; socre: 0.6510252904989747\n"
    }
   ],
   "source": [
    "results = []\n",
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=10)\n",
    "    cv_result = cross_val_score(model, X_new, y, cv=kfold)\n",
    "    results.append((name, cv_result))\n",
    "\n",
    "for result in results:\n",
    "    print(f\"name: {result[0]}; socre: {result[1].mean()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从输出可以看出，还是普通的k-均值模型准确性较高。与所有特征一起训练的准确性差不多，这侧面证明了SelectKBest特征选择的准确性。 \n",
    "回到目标上来，我们想看看为什么k-均值算法无法很好地拟合训练样本。现在只有2个特征，可以很方便地在二维坐标轴上画出所有的训练样本，观察这些数据的分布情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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     "metadata": {},
     "execution_count": 39
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 画出数据\n",
    "plt.figure()\n",
    "plt.ylabel('BMI')\n",
    "plt.xlabel('Glucose')\n",
    "# 画出 y==0 的阴性样本，用圆圈表示\n",
    "plt.scatter(X_new[y==0][:, 0], X_new[y==0][:, 1], c='r', s=20, marker='o')\n",
    "# 画出 y==1 的阳性样本，用三角形表示\n",
    "plt.scatter(X_new[y==1][:, 0], X_new[y==1][:, 1], c='g', s=20, marker='^')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "横坐标是血糖值，轴坐标是BMI值，反映身体肥胖情况。从图中可以看出，在中间数据集密集的区域，阳性和阴性样本几乎重叠在一起。假设现在有一个待预测的样本在中间密集区域，它的阳性邻居多还是阴性邻居多？这很难说，所以k-均值算法在这个糖尿病预测问题上，无法达到很高的预测准确性。"
   ]
  }
 ]
}